elo_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('nbaallelo.csv',index_col='game_id')\n",
    "df = df[df['game_location']!='A'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_team = {\n",
    "    'CHO':'CHA',\n",
    "    'NOP':'NOH',\n",
    "    'BRK':'NJN',\n",
    "    'OKC':'SEA',\n",
    "    'NOK':'NOH',\n",
    "    'NOH':'CHH',\n",
    "    'MEM':'VAN',\n",
    "    'WAS':'WSB',\n",
    "    'SAC':'KCK',\n",
    "    'LAC':'SDC',\n",
    "    'UTA':'NOJ',\n",
    "    'SDC':'BUF',\n",
    "    'NJN':'NYN',\n",
    "    'GSW':'SFW',\n",
    "    'DLC':'TEX',\n",
    "    'HOU':'SDR',\n",
    "    'CHA':'CHH',\n",
    "    'SAA':'TEX',\n",
    "    'SAS':'SAA',\n",
    "    'DEN':'DNA',\n",
    "    'DNA':'DNR',\n",
    "    'WSB':'CAP',\n",
    "    'CAP':'BAL',\n",
    "    'BAL':'CHZ',\n",
    "    'CHZ':'CHP',\n",
    "    'SDS':'SDA',\n",
    "    'FLO':'MMF',\n",
    "    'MMF':'MNM',\n",
    "    'SFW':'PHW',\n",
    "    'LAL':'MNL',\n",
    "    'LAS':'ANA',\n",
    "    'UTS':'LAS',\n",
    "    'CAR':'HSM',\n",
    "    'SSL':'CAR',\n",
    "    'DET':'FTW',\n",
    "    'MLH':'TRI',\n",
    "    'STL':'MLH',\n",
    "    'ATL':'STL',\n",
    "    'PHI':'SYR',\n",
    "    'CIN':'ROC',\n",
    "    'PTC':'PTP',\n",
    "    'MNP':'PTP',\n",
    "    'PTP':'MNP',\n",
    "    'MMP':'NOB',\n",
    "    'MMT':'MMP',\n",
    "    'MMS':'MMT',\n",
    "    'VIR':'WSA',\n",
    "    'WSA':'OAK'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7581997184419529, 0.7437955130012255)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def elo_acc_poly(K=20,HFA=100,SW=0.75,ELOW=400,C1=0,C2=0,C3=0,C4=1,C5=0):\n",
    "    NEW_TEAM=1300\n",
    "    elo = {}\n",
    "    year_id = 1947\n",
    "    x1s = []\n",
    "    x2s = []\n",
    "    movs = []\n",
    "    correct = 0\n",
    "    games = 0\n",
    "    for i,row in enumerate(df.itertuples()):\n",
    "        #if (i%2) == 1:\n",
    "        #    continue\n",
    "        if row[3] != 0:\n",
    "            continue\n",
    "        if row[4] != year_id:\n",
    "            for k in elo:\n",
    "                elo[k] = SW*elo[k] + (1-SW)*1500\n",
    "            year_id +=1\n",
    "            teams1 = set(list(df[df.year_id == year_id-1].team_id.unique()) + list(df[df.year_id == year_id-1].opp_id.unique()))\n",
    "\n",
    "            teams2 = set(list(df[df.year_id == year_id].team_id.unique()) + list(df[df.year_id == year_id].opp_id.unique()))\n",
    "            #print(len(teams1),len(teams2))\n",
    "            for t in [_ for _ in teams2 if not _ in teams1]:\n",
    "                if t in new_team and not t in elo and new_team[t] in elo:\n",
    "                    #print(t,new_team[t])\n",
    "                    elo[t] = elo[new_team[t]]\n",
    "\n",
    "        t1,t2=row[8],row[14]\n",
    "        elo_i,elo_n = row[11],row[12]\n",
    "        opp_elo_i,opp_elo_n = row[17],row[18]\n",
    "        p1,p2 = row[10],row[16]\n",
    "        elo1 = elo.get(t1,NEW_TEAM) + HFA\n",
    "        if row[-4] == 'N':\n",
    "            elo1 -= HFA\n",
    "        elo2 = elo.get(t2,NEW_TEAM)\n",
    "\n",
    "        winp = 1.0 / (np.exp(-(elo1-elo2)/ELOW) + 1)\n",
    "\n",
    "        mov = abs(p1-p2)\n",
    "        elo_diff_w  = (elo2 - elo1)\n",
    "\n",
    "        extra = (np.sqrt(mov)*C1 + mov*C2 + (mov**2)*C3)\n",
    "        extra /= C4 + C5*( (elo2 - elo1) if p2 > p1 else (elo1 - elo2))\n",
    "        \n",
    "        if p1 > p2:\n",
    "            hm,am = 1,-1\n",
    "            add_t = (K + extra)*(1-winp)\n",
    "        else:\n",
    "            hm,am = -1,1\n",
    "            add_t = (K + extra)*(winp)\n",
    "\n",
    "        if row[-4] == 'N':\n",
    "            elo[t1] = elo1 + hm*add_t\n",
    "        else:\n",
    "            elo[t1] = elo1 + hm*add_t - HFA\n",
    "\n",
    "        elo[t2] = elo2 + am*add_t\n",
    "        a = (winp if p1 > p2 else (1.0-winp))\n",
    "        b = int((elo1 > elo2) == (p1 > p2))\n",
    "        if year_id > 2004:\n",
    "            correct += (b+0.1*(1-(1-a)**2))\n",
    "            games += 1\n",
    "        else:\n",
    "            pass\n",
    "            correct += 0.25*(b+0.1*(1-(1-a)**2))\n",
    "            games += 0.25\n",
    "    return correct/games,elo\n",
    "def opt_elo_poly(x):\n",
    "    #print(x*xn)\n",
    "    #print(x[0])\n",
    "    return 1.0-elo_acc_poly(*x[0])[0]\n",
    "elo_acc_poly(29.76, 198.61, 0.50, 400.00, 5.00, -1.00, 1.00, 10.00, 0.00,  )[0],elo_acc_poly()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.751568918977521, 0.7557074001449813)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def elo_acc_pid(KP=20,KD=0,KI=0,HFA=100,SW=0.75,ELOW=400,LPF=1.0):\n",
    "    NEW_TEAM=1300\n",
    "    elo = {}\n",
    "    iterm = {}\n",
    "    dterm = {}\n",
    "    year_id = 1947\n",
    "    x1s = []\n",
    "    x2s = []\n",
    "    movs = []\n",
    "    correct = 0\n",
    "    games = 0\n",
    "    for i,row in enumerate(df.itertuples()):\n",
    "        #if (i%2) == 1:\n",
    "        #    continue\n",
    "        if row[3] != 0:\n",
    "            continue\n",
    "        if row[4] != year_id:\n",
    "            iterm = {}\n",
    "            dterm = {}\n",
    "            for k in elo:\n",
    "                elo[k] = SW*elo[k] + (1-SW)*1500\n",
    "            year_id +=1\n",
    "            teams1 = set(list(df[df.year_id == year_id-1].team_id.unique()) + list(df[df.year_id == year_id-1].opp_id.unique()))\n",
    "\n",
    "            teams2 = set(list(df[df.year_id == year_id].team_id.unique()) + list(df[df.year_id == year_id].opp_id.unique()))\n",
    "            #print(len(teams1),len(teams2))\n",
    "            for t in [_ for _ in teams2 if not _ in teams1]:\n",
    "                if t in new_team and not t in elo and new_team[t] in elo:\n",
    "                    #print(t,new_team[t])\n",
    "                    elo[t] = elo[new_team[t]]\n",
    "  \n",
    "\n",
    "\n",
    "        t1,t2=row[8],row[14]\n",
    "        elo_i,elo_n = row[11],row[12]\n",
    "        opp_elo_i,opp_elo_n = row[17],row[18]\n",
    "        p1,p2 = row[10],row[16]\n",
    "        elo1 = elo.get(t1,NEW_TEAM) + HFA\n",
    "        if row[-4] == 'N':\n",
    "            elo1 -= HFA\n",
    "        elo2 = elo.get(t2,NEW_TEAM)\n",
    "\n",
    "        winp = 1.0 / (np.exp(-(elo1-elo2)/ELOW) + 1)\n",
    "\n",
    "        mov = abs(p1-p2)\n",
    "        elo_diff_w  = (elo2 - elo1)\n",
    "\n",
    "        if p1 > p2:\n",
    "            hm,am = 1,-1\n",
    "            add_t = (1-winp)\n",
    "        else:\n",
    "            hm,am = -1,1\n",
    "            add_t = (winp)\n",
    "\n",
    "        #print(mofv_m,mov)\n",
    "        if row[-4] == 'N':\n",
    "            elo[t1] = elo1 + KP*hm*add_t + KD*(hm*add_t - dterm.get(t1,hm*add_t)) + KI*iterm.get(t1,0)\n",
    "        else:\n",
    "            elo[t1] = elo1 + KP*hm*add_t - HFA + KD*(hm*add_t - dterm.get(t1,hm*add_t)) + KI*iterm.get(t1,0)\n",
    "\n",
    "        elo[t2] = elo2 + KP*am*add_t + KD*(am*add_t - dterm.get(t2,am*add_t)) + KI*iterm.get(t2,0)\n",
    "        dterm[t1] = LPF*hm*add_t + (1-LPF)*dterm.get(t1,hm*add_t)\n",
    "        dterm[t2] = LPF*am*add_t + (1-LPF)*dterm.get(t2,am*add_t)\n",
    "        iterm[t1] = iterm.get(t1,0) + hm*add_t\n",
    "        iterm[t2] = iterm.get(t2,0) + am*add_t\n",
    "        a = (winp if p1 > p2 else (1.0-winp))\n",
    "        b = int((elo1 > elo2) == (p1 > p2))\n",
    "        if year_id > 2004:\n",
    "            #a = a ** (1/3)\n",
    "            correct += (b+0.1*(1-(1-a)**2))\n",
    "            games += 1\n",
    "        else:\n",
    "            #pass\n",
    "            correct += 0.25*(b+0.1*(1-(1-a)**2))\n",
    "            games += 0.25* 1.0\n",
    "        #print(elo1,elo2)\n",
    "        #print(p1,p2)\n",
    "        #print(winp)\n",
    "    return correct/games,elo\n",
    "def opt_elo_pid(x):\n",
    "    #print(x*xn)\n",
    "    #print(x[0])\n",
    "    return 1.0-elo_acc_pid(*x[0])[0]\n",
    "elo_acc_pid(120.05, 7.21, 0.17, 247.69, 0.70, 400.00, 0.94, )[0],elo_acc_pid(81.41230187,   0.        ,   0.        , 241.40955643,       0.5       , 465.8921979)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7437955130012255, 0.7557618558080772)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def elo_acc_simple(K=20,HFA=100,SW=0.75,ELOW=400): # 173.71\n",
    "    NEW_TEAM=1300\n",
    "    elo = {}\n",
    "    year_id = 1947\n",
    "    x1s = []\n",
    "    x2s = []\n",
    "    movs = []\n",
    "    correct = 0\n",
    "    games = 0\n",
    "    for i,row in enumerate(df.itertuples()):\n",
    "        #if (i%2) == 1:\n",
    "        #    continue\n",
    "        if row[3] != 0:\n",
    "            continue\n",
    "        if row[4] != year_id:\n",
    "            for k in elo:\n",
    "                elo[k] = SW*elo[k] + (1-SW)*1500\n",
    "            year_id +=1\n",
    "            teams1 = set(list(df[df.year_id == year_id-1].team_id.unique()) + list(df[df.year_id == year_id-1].opp_id.unique()))\n",
    "\n",
    "            teams2 = set(list(df[df.year_id == year_id].team_id.unique()) + list(df[df.year_id == year_id].opp_id.unique()))\n",
    "            #print(len(teams1),len(teams2))\n",
    "            for t in [_ for _ in teams2 if not _ in teams1]:\n",
    "                if t in new_team and not t in elo and new_team[t] in elo:\n",
    "                    #print(t,new_team[t])\n",
    "                    elo[t] = elo[new_team[t]]\n",
    "\n",
    "        t1,t2=row[8],row[14]\n",
    "        elo_i,elo_n = row[11],row[12]\n",
    "        opp_elo_i,opp_elo_n = row[17],row[18]\n",
    "        p1,p2 = row[10],row[16]\n",
    "        elo1 = elo.get(t1,NEW_TEAM) + HFA\n",
    "        if row[-4] == 'N':\n",
    "            elo1 -= HFA\n",
    "        elo2 = elo.get(t2,NEW_TEAM)\n",
    "\n",
    "        winp = 1.0 / (np.exp(-(elo1-elo2)/ELOW) + 1)\n",
    "\n",
    "        mov = abs(p1-p2)\n",
    "        elo_diff_w  = (elo2 - elo1)\n",
    "\n",
    "        if p1 > p2:\n",
    "            hm,am = 1,-1\n",
    "            add_t = K*(1-winp)\n",
    "        else:\n",
    "            hm,am = -1,1\n",
    "            add_t = K*(winp)\n",
    "\n",
    "        #print(mofv_m,mov)\n",
    "        if row[-4] == 'N':\n",
    "            elo[t1] = elo1 + hm*add_t\n",
    "        else:\n",
    "            elo[t1] = elo1 + hm*add_t - HFA\n",
    "\n",
    "        elo[t2] = elo2 + am*add_t\n",
    "        a = (winp if p1 > p2 else (1.0-winp))\n",
    "        b = int((elo1 > elo2) == (p1 > p2))\n",
    "        if year_id > 2004:\n",
    "            #a = a ** (1/3)\n",
    "            correct += (b+0.1*(1-(1-a)**2))\n",
    "            games += 1\n",
    "        else:\n",
    "            pass\n",
    "            #a = a ** (1/3)\n",
    "            correct += 0.25*(b+0.1*(1-(1-a)**2))\n",
    "            games += 0.25\n",
    "        #print(elo1,elo2)\n",
    "        #print(p1,p2)\n",
    "        #print(winp)\n",
    "    return correct/games,elo\n",
    "def opt_elo_simple(x):\n",
    "    #print(x*xn)\n",
    "    #print(x[0])\n",
    "    return 1.0-elo_acc_simple(*x[0])[0]\n",
    "elo_acc_simple()[0],elo_acc_simple( 70.76, 208.57, 0.50, )[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7560086644917214,\n",
       " 0.7570263289129908,\n",
       " 0.7550033284611638,\n",
       " 0.7585252794137248,\n",
       " 0.7593310220737157,\n",
       " 0.7549398189059956,\n",
       " 0.7595215389864719)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def elo_acc(K=20,HFA=100,SW=0.75,MOVM=3,ELOW=400):\n",
    "    INTERCEPT=7.5\n",
    "    SLOPE=0.006\n",
    "    POW=0.8\n",
    "    NEW_TEAM=1300\n",
    "    elo = {}\n",
    "    year_id = 1947\n",
    "    ELOW=400\n",
    "    x1s = []\n",
    "    x2s = []\n",
    "    movs = []\n",
    "    correct = 0\n",
    "    games = 0\n",
    "    for i,row in enumerate(df.itertuples()):\n",
    "        #if (i%2) == 1:\n",
    "        #    continue\n",
    "        if row[3] != 0:\n",
    "            continue\n",
    "        if row[4] != year_id:\n",
    "            for k in elo:\n",
    "                elo[k] = SW*elo[k] + (1-SW)*1505\n",
    "            year_id +=1\n",
    "            teams1 = set(list(df[df.year_id == year_id-1].team_id.unique()) + list(df[df.year_id == year_id-1].opp_id.unique()))\n",
    "\n",
    "            teams2 = set(list(df[df.year_id == year_id].team_id.unique()) + list(df[df.year_id == year_id].opp_id.unique()))\n",
    "            #print(len(teams1),len(teams2))\n",
    "            for t in [_ for _ in teams2 if not _ in teams1]:\n",
    "                if t in new_team and not t in elo and new_team[t] in elo:\n",
    "                    #print(t,new_team[t])\n",
    "                    elo[t] = elo[new_team[t]]\n",
    "\n",
    "        t1,t2=row[8],row[14]\n",
    "        elo_i,elo_n = row[11],row[12]\n",
    "        opp_elo_i,opp_elo_n = row[17],row[18]\n",
    "        p1,p2 = row[10],row[16]\n",
    "        elo1 = elo.get(t1,NEW_TEAM) + HFA\n",
    "        if row[-4] == 'N':\n",
    "            elo1 -= HFA\n",
    "        elo2 = elo.get(t2,NEW_TEAM)\n",
    "        #if t1 not in elo:\n",
    "        #    print(t1,row[9],len(elo),year_id)\n",
    "        #if t2 not in elo:\n",
    "        #    print(t2,row[15],len(elo),year_id)\n",
    "        winp = 1.0 / (10 ** (-(elo1-elo2)/ELOW) + 1)\n",
    "\n",
    "        mov = abs(p1-p2)\n",
    "        elo_diff_w  = (elo2 - elo1)\n",
    "        mofv_m1 = ((mov+MOVM) ** POW)/(INTERCEPT + SLOPE*(-elo_diff_w))\n",
    "        mofv_m2 = ((mov+MOVM) ** POW)/(INTERCEPT + SLOPE*(elo_diff_w))\n",
    "\n",
    "        if p1 > p2:\n",
    "            hm,am = 1,-1\n",
    "            add_t = K*(1-winp)*mofv_m1\n",
    "        else:\n",
    "            hm,am = -1,1\n",
    "            add_t = K*(winp)*mofv_m2\n",
    "\n",
    "        #print(mofv_m,mov)\n",
    "        if row[-4] == 'N':\n",
    "            elo[t1] = elo1 + hm*add_t\n",
    "        else:\n",
    "            elo[t1] = elo1 + hm*add_t - HFA\n",
    "\n",
    "        elo[t2] = elo2 + am*add_t\n",
    "        a = (winp if p1 > p2 else (1.0-winp))\n",
    "        b = int((elo1 > elo2) == (p1 > p2))\n",
    "        if year_id > 2004:\n",
    "            #a = a ** (1/3)\n",
    "            correct += (b+0.1*(1-(1-a)**2))\n",
    "            games += 1\n",
    "        else:\n",
    "            pass\n",
    "            #a = a ** (1/3)\n",
    "            correct += 0.25*(b+0.1*(1-(1-a)**2))\n",
    "            games += 0.25\n",
    "        #print(elo1,elo2)\n",
    "        #print(p1,p2)\n",
    "        #print(winp)\n",
    "    return correct/games,elo\n",
    "def opt_elo(x):\n",
    "    #print(x*xn)\n",
    "    #print(x[0])\n",
    "    return 1.0-elo_acc(*x[0])[0]\n",
    "elo_acc(20,100,0.75,3,400)[0],elo_acc(42,107,0.95,0,400)[0],elo_acc(53,155,0.95,0,400.)[0],elo_acc(22.6,81.5,0.5,0.,400.)[0],elo_acc(34,100,0.5,0.,400.)[0],elo_acc(4.        ,  81.94891483,   0.95      , 134.72834631)[0],elo_acc(34.01, 99.01, 0.50, 0.00, 400.00,  )[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSW\t1823.57\n",
      "SAS\t1721.05\n",
      "LAC\t1694.63\n",
      "CLE\t1690.98\n",
      "HOU\t1654.73\n",
      "MEM\t1608.44\n",
      "CHI\t1591.14\n",
      "OKC\t1583.32\n",
      "ATL\t1580.88\n",
      "POR\t1556.83\n",
      "DAL\t1556.67\n",
      "UTA\t1555.38\n",
      "WAS\t1546.20\n",
      "NOP\t1526.66\n",
      "BOS\t1524.66\n",
      "IND\t1504.96\n",
      "TOR\t1500.49\n",
      "PHO\t1466.93\n",
      "DET\t1461.17\n",
      "BRK\t1457.83\n",
      "MIA\t1455.95\n",
      "MIL\t1443.36\n",
      "DEN\t1422.45\n",
      "SAC\t1418.37\n",
      "CHO\t1400.82\n",
      "ORL\t1311.98\n",
      "LAL\t1283.33\n",
      "PHI\t1276.28\n",
      "MIN\t1264.05\n",
      "NYK\t1255.78\n",
      "\n",
      "\n",
      "GSW\t1876.22\n",
      "SAS\t1755.99\n",
      "CLE\t1719.17\n",
      "LAC\t1707.60\n",
      "HOU\t1676.00\n",
      "CHI\t1615.95\n",
      "MEM\t1610.04\n",
      "OKC\t1588.58\n",
      "UTA\t1588.11\n",
      "ATL\t1577.80\n",
      "WAS\t1555.58\n",
      "DAL\t1553.97\n",
      "BOS\t1550.82\n",
      "POR\t1542.26\n",
      "NOP\t1526.96\n",
      "IND\t1508.38\n",
      "TOR\t1487.98\n",
      "DET\t1471.27\n",
      "MIA\t1450.30\n",
      "PHO\t1443.92\n",
      "BRK\t1438.54\n",
      "MIL\t1432.71\n",
      "DEN\t1429.60\n",
      "SAC\t1424.53\n",
      "CHO\t1374.75\n",
      "ORL\t1299.98\n",
      "PHI\t1269.37\n",
      "LAL\t1243.63\n",
      "MIN\t1217.45\n",
      "NYK\t1212.31\n",
      "\n",
      "\n",
      "GSW\t2394.99\n",
      "CLE\t2122.88\n",
      "SAS\t2021.31\n",
      "HOU\t2013.77\n",
      "LAC\t2013.59\n",
      "MEM\t1860.01\n",
      "ATL\t1756.41\n",
      "CHI\t1675.89\n",
      "OKC\t1657.50\n",
      "DAL\t1656.42\n",
      "WAS\t1624.91\n",
      "NOP\t1612.21\n",
      "BOS\t1605.86\n",
      "UTA\t1587.57\n",
      "IND\t1556.26\n",
      "POR\t1555.48\n",
      "BRK\t1497.49\n",
      "TOR\t1431.43\n",
      "MIL\t1392.71\n",
      "PHO\t1361.17\n",
      "MIA\t1354.31\n",
      "DET\t1324.47\n",
      "DEN\t1217.21\n",
      "CHO\t1185.68\n",
      "SAC\t1170.50\n",
      "ORL\t977.08\n",
      "LAL\t897.22\n",
      "NYK\t869.73\n",
      "PHI\t803.07\n",
      "MIN\t802.07\n",
      "\n",
      "\n",
      "GSW\t1876.22\n",
      "SAS\t1755.99\n",
      "CLE\t1719.17\n",
      "LAC\t1707.60\n",
      "HOU\t1676.00\n",
      "CHI\t1615.95\n",
      "MEM\t1610.04\n",
      "OKC\t1588.58\n",
      "UTA\t1588.11\n",
      "ATL\t1577.80\n",
      "WAS\t1555.58\n",
      "DAL\t1553.97\n",
      "BOS\t1550.82\n",
      "POR\t1542.26\n",
      "NOP\t1526.96\n",
      "IND\t1508.38\n",
      "TOR\t1487.98\n",
      "DET\t1471.27\n",
      "MIA\t1450.30\n",
      "PHO\t1443.92\n",
      "BRK\t1438.54\n",
      "MIL\t1432.71\n",
      "DEN\t1429.60\n",
      "SAC\t1424.53\n",
      "CHO\t1374.75\n",
      "ORL\t1299.98\n",
      "PHI\t1269.37\n",
      "LAL\t1243.63\n",
      "MIN\t1217.45\n",
      "NYK\t1212.31\n"
     ]
    }
   ],
   "source": [
    "_,elo =elo_acc(20,100,0.75,3,400)\n",
    "rests = []\n",
    "for t in df[df.year_id == 2015].team_id.unique():\n",
    "    #print(t,elo[t])\n",
    "    rests.append((elo[t],t))\n",
    "for s,t in sorted(rests,reverse=True):\n",
    "    print('{}\\t{:.2f}'.format(t,s))\n",
    "_,elo=elo_acc(34,100,0.5,0.,400.)\n",
    "rests = []\n",
    "print('\\n')\n",
    "for t in df[df.year_id == 2015].team_id.unique():\n",
    "    #print(t,elo[t])\n",
    "    rests.append((elo[t],t))\n",
    "for s,t in sorted(rests,reverse=True):\n",
    "    print('{}\\t{:.2f}'.format(t,s))\n",
    "_,elo= elo_acc_pid(81.41230187,   0.        ,   0.        , 241.40955643,       0.5       , 465.8921979)\n",
    "rests = []\n",
    "print('\\n')\n",
    "for t in df[df.year_id == 2015].team_id.unique():\n",
    "    #print(t,elo[t])\n",
    "    rests.append((elo[t],t))\n",
    "for s,t in sorted(rests,reverse=True):\n",
    "    print('{}\\t{:.2f}'.format(t,s))\n",
    "_,elo =elo_acc(34,100,0.5,0.,400.)\n",
    "rests = []\n",
    "print('\\n')\n",
    "for t in df[df.year_id == 2015].team_id.unique():\n",
    "    #print(t,elo[t])\n",
    "    rests.append((elo[t],t))\n",
    "for s,t in sorted(rests,reverse=True):\n",
    "    print('{}\\t{:.2f}'.format(t,s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The set cost function is ignored! LCB acquisition does not make sense with cost.\n",
      "num acquisition: 1, time elapsed: 1.16s\n",
      "num acquisition: 2, time elapsed: 2.36s\n",
      "num acquisition: 3, time elapsed: 3.53s\n",
      "num acquisition: 4, time elapsed: 4.73s\n",
      "num acquisition: 5, time elapsed: 6.05s\n",
      "num acquisition: 6, time elapsed: 7.37s\n",
      "num acquisition: 7, time elapsed: 8.59s\n",
      "num acquisition: 8, time elapsed: 9.79s\n",
      "num acquisition: 9, time elapsed: 11.22s\n",
      "num acquisition: 10, time elapsed: 12.60s\n",
      "num acquisition: 11, time elapsed: 14.01s\n",
      "num acquisition: 12, time elapsed: 15.30s\n",
      "num acquisition: 13, time elapsed: 16.66s\n",
      "num acquisition: 14, time elapsed: 17.95s\n",
      "num acquisition: 15, time elapsed: 19.29s\n",
      "num acquisition: 16, time elapsed: 20.52s\n",
      "num acquisition: 17, time elapsed: 21.68s\n",
      "num acquisition: 18, time elapsed: 22.86s\n",
      "num acquisition: 19, time elapsed: 24.21s\n",
      "num acquisition: 20, time elapsed: 25.47s\n",
      "num acquisition: 21, time elapsed: 26.72s\n",
      "num acquisition: 22, time elapsed: 27.95s\n",
      "num acquisition: 23, time elapsed: 29.16s\n",
      "num acquisition: 24, time elapsed: 30.33s\n",
      "num acquisition: 25, time elapsed: 31.49s\n",
      "num acquisition: 26, time elapsed: 32.76s\n",
      "num acquisition: 27, time elapsed: 33.95s\n",
      "num acquisition: 28, time elapsed: 35.15s\n",
      "num acquisition: 29, time elapsed: 36.40s\n",
      "num acquisition: 30, time elapsed: 37.74s\n",
      "num acquisition: 31, time elapsed: 38.97s\n",
      "num acquisition: 32, time elapsed: 40.18s\n",
      "num acquisition: 33, time elapsed: 41.48s\n",
      "num acquisition: 34, time elapsed: 42.83s\n",
      "num acquisition: 35, time elapsed: 44.10s\n",
      "num acquisition: 36, time elapsed: 45.36s\n",
      "num acquisition: 37, time elapsed: 46.62s\n",
      "num acquisition: 38, time elapsed: 47.93s\n",
      "num acquisition: 39, time elapsed: 49.11s\n",
      "num acquisition: 40, time elapsed: 50.28s\n",
      "num acquisition: 41, time elapsed: 51.45s\n",
      "num acquisition: 42, time elapsed: 52.72s\n",
      "num acquisition: 43, time elapsed: 54.05s\n",
      "num acquisition: 44, time elapsed: 55.33s\n",
      "num acquisition: 45, time elapsed: 56.62s\n",
      "num acquisition: 46, time elapsed: 57.94s\n",
      "num acquisition: 47, time elapsed: 59.16s\n",
      "num acquisition: 48, time elapsed: 60.43s\n"
     ]
    }
   ],
   "source": [
    "import GPyOpt\n",
    "import GPy\n",
    "#elo_acc_simple(K=20,HFA=100,SW=0.75,ELOW=400):\n",
    "#bounds = [(10,60),(0,200),(0.5,0.95),(0,6),(400,400)] #normal\n",
    "bounds = [(10,100),(100,300),(0.5,0.95),(150,400)] #,(200,500) #simple\n",
    "\n",
    "#bounds = [(10,200),(-10,10),(-10,10),(0,600),(0.5,0.95),(400,400),(0,1)] #pid\n",
    "#bounds = [(10,100),(50,300),(0.5,0.95),(400,400),(-5,5),(-1,1),(-1,1),(1,10),(0,1)] #poly\n",
    "bounds2 = [{'name': 'var_{}'.format(i), 'type': 'continuous', 'domain': _} for i,_ in enumerate(bounds)]  # problem constraints \n",
    "myBopt = GPyOpt.methods.BayesianOptimization(f=opt_elo_simple,            # function to optimize\n",
    "                                             model_type='GP',\n",
    "                                             domain=bounds2,        # box-constraints of the problem\n",
    "                                             initial_design_numdata=10,\n",
    "                                             acquisition_type='LCB',\n",
    "                                             exact_feval = True,\n",
    "                                                num_cores=2,\n",
    "                                                verbosity=True) # Selects the Expected improvement\n",
    "max_iter = 1500     # evaluation budget\n",
    "max_time = 60     # time budget \n",
    "eps      = 10e-6  # Minimum allows distance between the las two observations\n",
    "\n",
    "myBopt.run_optimization(max_iter, max_time, eps,verbosity=True)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myBopt.plot_convergence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41.00, 121.67, 0.50, 226.88, "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.755334441965373"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for a in myBopt.x_opt:\n",
    "    print('{:.2f},'.format(a),end=' ')\n",
    "elo_acc_simple(*myBopt.x_opt)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gameorder</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>...</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201506090CLE</th>\n",
       "      <td>63154</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/9/2015</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>61.826408</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>91</td>\n",
       "      <td>1797.5032</td>\n",
       "      <td>1790.9591</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.527843</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201506110CLE</th>\n",
       "      <td>63155</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/11/2015</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>60.309792</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>103</td>\n",
       "      <td>1790.9591</td>\n",
       "      <td>1809.9791</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.546572</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201506140GSW</th>\n",
       "      <td>63156</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>104</td>\n",
       "      <td>...</td>\n",
       "      <td>68.013329</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>91</td>\n",
       "      <td>1704.3949</td>\n",
       "      <td>1700.7391</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.765565</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201506170CLE</th>\n",
       "      <td>63157</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6/16/2015</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>59.290245</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>105</td>\n",
       "      <td>1813.6349</td>\n",
       "      <td>1822.2881</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.481450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              gameorder lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "game_id                                                                  \n",
       "201506090CLE      63154   NBA        0     2015   6/9/2015          99   \n",
       "201506110CLE      63155   NBA        0     2015  6/11/2015         100   \n",
       "201506140GSW      63156   NBA        0     2015  6/14/2015         102   \n",
       "201506170CLE      63157   NBA        0     2015  6/16/2015         102   \n",
       "\n",
       "              is_playoffs team_id    fran_id  pts  ...  win_equiv  opp_id  \\\n",
       "game_id                                            ...                      \n",
       "201506090CLE            1     CLE  Cavaliers   96  ...  61.826408     GSW   \n",
       "201506110CLE            1     CLE  Cavaliers   82  ...  60.309792     GSW   \n",
       "201506140GSW            1     GSW   Warriors  104  ...  68.013329     CLE   \n",
       "201506170CLE            1     CLE  Cavaliers   97  ...  59.290245     GSW   \n",
       "\n",
       "               opp_fran opp_pts  opp_elo_i  opp_elo_n  game_location  \\\n",
       "game_id                                                                \n",
       "201506090CLE   Warriors      91  1797.5032  1790.9591              H   \n",
       "201506110CLE   Warriors     103  1790.9591  1809.9791              H   \n",
       "201506140GSW  Cavaliers      91  1704.3949  1700.7391              H   \n",
       "201506170CLE   Warriors     105  1813.6349  1822.2881              H   \n",
       "\n",
       "              game_result  forecast notes  \n",
       "game_id                                    \n",
       "201506090CLE            W  0.527843   NaN  \n",
       "201506110CLE            L  0.546572   NaN  \n",
       "201506140GSW            W  0.765565   NaN  \n",
       "201506170CLE            L  0.481450   NaN  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "prev_years = set()\n",
    "prev_years2 = set()\n",
    "if False:\n",
    "    for year in sorted(df.year_id.unique()):\n",
    "        teams = set(list(df[df.year_id == year].fran_id.unique()) + list(df[df.year_id == year].opp_fran.unique()))\n",
    "        teams2 = set(list(df[df.year_id == year].team_id.unique()) + list(df[df.year_id == year].opp_id.unique()))\n",
    "\n",
    "        if year > 1947:\n",
    "            for t in prev_years:\n",
    "                if not t in teams:\n",
    "                    print('\\t',t,year)\n",
    "            for t in teams:\n",
    "                if not t in prev_years:\n",
    "                    print(t,year)\n",
    "\n",
    "            for t in prev_years2:\n",
    "                if not t in teams2:\n",
    "                    print('\\t',t,year)\n",
    "            for t in teams2:\n",
    "                if not t in prev_years2:\n",
    "                    print(t,year) \n",
    "        prev_years = teams #prev_years.union(teams)\n",
    "        prev_years2 = teams2 #prev_years2.union(teams2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(173.71779276130073, 2.302585092994046)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(1/(1+np.exp(-np.linspace(-10,10))))\n",
    "x = np.linspace(-1000,1000)\n",
    "scaled = 400/np.log(10)\n",
    "plt.plot(x,1/(1+10**(-x/400)))\n",
    "plt.plot(x,1/(1+np.exp(-x/scaled)))\n",
    "scaled,np.log(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.21984126984126987,\n",
       "  0.1787661406025825,\n",
       "  0.3055555555555556,\n",
       "  0.08571428571428572,\n",
       "  0.4470588235294118,\n",
       "  13.996825396825397,\n",
       "  9.700000000000001,\n",
       "  -3.6,\n",
       "  -2.888888888888889,\n",
       "  37,\n",
       "  True,\n",
       "  False],\n",
       " True)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "# B. Morris says logistic regression does better, let's see! \n",
    "Xd = []\n",
    "yd = []\n",
    "movs = defaultdict(list)\n",
    "season_averages = {}\n",
    "previous_season_average = {}\n",
    "year_id = 1947\n",
    "for i,row in enumerate(df.itertuples()):\n",
    "    # duplicate\n",
    "    if row[3] != 0:\n",
    "        continue\n",
    "    # new year\n",
    "    if row[4] != year_id:\n",
    "        year_id +=1\n",
    "        teams1 = set(list(df[df.year_id == year_id-1].team_id.unique()) + list(df[df.year_id == year_id-1].opp_id.unique()))\n",
    "        teams2 = set(list(df[df.year_id == year_id].team_id.unique()) + list(df[df.year_id == year_id].opp_id.unique()))\n",
    "        previous_season_average = season_averages\n",
    "        season_averages = {}\n",
    "        movs = defaultdict(list)\n",
    "        for t in [_ for _ in teams2 if not _ in teams1]:\n",
    "            if t in new_team and not t in previous_season_average and new_team[t] in previous_season_average:\n",
    "                previous_season_average[t] = previous_season_average[new_team[t]]\n",
    "\n",
    "    t1,t2=row[8],row[14]\n",
    "    elo_i,elo_n = row[11],row[12]\n",
    "    opp_elo_i,opp_elo_n = row[17],row[18]\n",
    "    p1,p2 = row[10],row[16]\n",
    "    t1s = season_averages.get(t1,[])\n",
    "    t2s = season_averages.get(t2,[])\n",
    "    pt1s = previous_season_average.get(t1,[])\n",
    "    pt2s = previous_season_average.get(t2,[])\n",
    "    t1m = movs[t1]\n",
    "    t2m = movs[t2]\n",
    "    \n",
    "    mov = p1 - p2\n",
    "    wp1 = sum(t1s)/np.maximum(1,len(t1s))\n",
    "    wp2 = sum(t2s)/np.maximum(1,len(t2s))\n",
    "    pwp1 = sum(pt1s)/np.maximum(1,len(pt1s))\n",
    "    pwp2 = sum(pt2s)/np.maximum(1,len(pt2s))\n",
    "\n",
    "    nr1 = sum(t1m)/np.maximum(1,len(t1m))\n",
    "    nr2 = sum(t2m)/np.maximum(1,len(t2m))\n",
    "    nr1r = sum(t1m[-10:])/np.maximum(1,len(t1m[-10:]))\n",
    "    nr2r = sum(t2m[-10:])/np.maximum(1,len(t2m[-10:]))\n",
    "    \n",
    "    if mov > 0:\n",
    "        t1s += [1]\n",
    "        t2s += [0]\n",
    "    else:\n",
    "        t1s += [0]\n",
    "        t2s += [1]\n",
    "\n",
    "    season_averages[t1] = t1s\n",
    "    season_averages[t2] = t2s\n",
    "    movs[t1].append(mov)\n",
    "    movs[t2].append(-mov)\n",
    "    \n",
    "    yd.append(mov > 0)\n",
    "    Xd.append([wp1-wp2,pwp1-pwp2,wp1,wp2,pwp1,nr1-nr2,nr1r-nr2r,nr1r,nr1,len(t1s)] + [year_id > 1980, year_id > 2000])\n",
    "    \n",
    "Xd[35000],yd[35000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import preprocessing\n",
    "from sklearn import neural_network\n",
    "from sklearn import ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xd = np.array(Xd)\n",
    "yd = np.array(yd)#[:,np.newaxis]\n",
    "fexp = preprocessing.PolynomialFeatures(degree=3,interaction_only=True)\n",
    "scalerX = preprocessing.StandardScaler()\n",
    "prescale_X = scalerX.fit_transform(Xd)\n",
    "prescale_X = fexp.fit_transform(prescale_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = linear_model.LogisticRegression()\n",
    "clf.fit(prescale_X,yd)\n",
    "\n",
    "#clf.fit(prescale_X[:-7647],yd[:-7647])\n",
    "#clf = ensemble.ExtraTreesClassifier(40,max_depth=12)\n",
    "#clf.fit(Xd[:-7647],yd[:-7647])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6796129200993853, 0.20526936475170218)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = clf.predict_proba(prescale_X[-7647:])\n",
    "a = clf.score(prescale_X[-7647:],yd[-7647:])\n",
    "\n",
    "a, ((1-b[np.arange(b.shape[0]).astype(np.int),yd[-7647:].astype(np.int)])**2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(b[np.arange(b.shape[0]).astype(np.int),yd[-7647:].astype(np.int)],100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    import scipy.optimize as opt\n",
    "\n",
    "    def scale_logit(s):\n",
    "        b = clf.predict_proba(prescale_X[-7647:])\n",
    "        xv = -np.log(1/b-1)\n",
    "        b = 1/(1+np.exp(-xv*s))\n",
    "        return ((1-b[np.arange(b.shape[0]).astype(np.int),yd[-7647:].astype(np.int)])**2).mean()\n",
    "\n",
    "    res = opt.basinhopping(scale_logit,1)\n",
    "    # new mean winp accuracy\n",
    "    1-scale_logit(res.x),res.x[0],(b[np.arange(b.shape[0]).astype(np.int),yd[-7647:].astype(np.int)]).mean()\n",
    "    # make sure the scaling produces\n",
    "    b = clf.predict_proba(prescale_X[-7647:])\n",
    "    xv = -np.log(1/b-1)\n",
    "    b = 1/(1+np.exp(-xv*res.x))\n",
    "    # same classifier!\n",
    "    abs(((b > 0.5).astype(np.int)[:,0]-yd[-7647:])).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6818721598555979, 0.61, 0.6779217379924094, 0.6159781576126065)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# full data\n",
    "aS = (0.59,0.61)\n",
    "bS= (0.6807321437053692, 0.6818721598555979)\n",
    "\n",
    "bSp2010 = (0.6726868210967151, 0.6779217379924094)\n",
    "aSp2010 = (0.593289400989701, 0.6159781576126065)\n",
    "max(bS),max(aS),max(bSp2010),max(aSp2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6790898391526089, 0.5888006476797119)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.6790898391526089, 0.5888006476797119)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'home team winning odds')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(b[:,1],100,density=True)\n",
    "plt.title('home team winning odds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE_ERRORS = \\\n",
    "(0.6073681755278523,\n",
    " 0.5795554325818568,\n",
    " 0.5537803066130098,\n",
    " 0.6244105711098151,\n",
    " 0.6031986734899218,\n",
    " 0.6172620987567141)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7436895829182865"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w  =np.ones(yd.shape[0])\n",
    "w[14220:] = 0.25\n",
    "b = clf.predict_proba(prescale_X)\n",
    "a = np.argmax(b,1)\n",
    "1-(abs(a-yd)*w).sum()/w.sum() + 0.1 * (w*(1-b[np.arange(b.shape[0]).astype(np.int),yd.astype(np.int)]**2)).sum()/w.sum()\n",
    "# (w*(1-b[np.arange(b.shape[0]).astype(np.int),yd.astype(np.int)]**2)).sum()/w.sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6852922083062843"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-abs(a-yd).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import autograd.numpy as np\n",
    "import autograd\n",
    "Y = yd.astype(np.float)\n",
    "W = 0.1*np.random.normal(size=prescale_X.shape[1] + 1)\n",
    "#W[:-1] = clf.coef_[0]\n",
    "#W[-1] = clf.intercept_\n",
    "INP = np.hstack([prescale_X,np.ones(prescale_X.shape[0]).reshape((-1,1))]).astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0350892881247427"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmoid = lambda x: 1/(1+np.exp(-x))\n",
    "def loss_f(x):\n",
    "    p = sigmoid(INP @ x)\n",
    "    # + \n",
    "    loss = np.mean(-Y * np.log(p) - (1-Y) *np.log(1-p+1e-12)) + 0.1*((w*(((1-Y)-p)**2)).sum()/w.sum()) #+ 1e-3 * (W[:-1]**2).sum()\n",
    "    return loss\n",
    "GRAD = autograd.jacobian(loss_f)\n",
    "loss_f(W)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0350892881247427\n",
      "0.9178815466909324\n",
      "0.9079203009761472\n",
      "0.8933597844672584\n",
      "0.8758129254768904\n",
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      "0.8413550903230931\n",
      "0.8292363786130902\n",
      "0.8211568873979533\n",
      "0.8147843932293043\n",
      "0.807338796855311\n",
      "0.7974232008461853\n",
      "0.7857813027517278\n",
      "0.7744246246720858\n",
      "0.7649528681339\n",
      "0.7577041693271827\n",
      "0.7518940369919435\n",
      "0.7463513339808296\n",
      "0.7402239874699894\n",
      "0.7332906488410112\n",
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      "0.7134181550180868\n",
      "0.709540150143826\n",
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      "0.7044814225284902\n",
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      "0.6972704957456461\n",
      "0.6939811683103212\n",
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      "0.6906151607175691\n",
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      "0.6800415888466406\n",
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      "0.6740206148980257\n",
      "0.6724650253852038\n",
      "0.6712912740473075\n",
      "0.6704200188710538\n",
      "0.669550007852339\n",
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      "0.6672069829558335\n",
      "0.6659844459508923\n",
      "0.664982053500377\n",
      "0.6641852529885083\n",
      "0.6633983827643423\n",
      "0.6625016575954109\n",
      "0.6616077706204299\n",
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      "0.6510796425785322\n",
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      "0.6500260599084438\n",
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      "0.6493347476135649\n",
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      "0.643999657288465\n",
      "0.6437886062884997\n",
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      "0.6287273404198082\n"
     ]
    }
   ],
   "source": [
    "alpha = 1e-1\n",
    "v = np.zeros_like(W)\n",
    "for i in range(100):\n",
    "    loss = loss_f(W)\n",
    "    v = 0.9*v - GRAD(W)\n",
    "    W += (alpha) * v\n",
    "    if (i%1000== 0):\n",
    "        alpha /= 10\n",
    "    print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37350.74401440111"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = sigmoid(INP @ W)\n",
    "loss = np.sum(-Y * np.log(p) - (1-Y) *np.log(1-p+1e-14)) #+ 0.1*((w*(((1-yd)-p)**2)).sum()/w.sum())\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7447699376025465"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = sigmoid(INP @ W)\n",
    "a = (p > 0.5).astype(np.float)\n",
    "1-(abs(a-yd)*w).sum()/w.sum() + 0.1*(1-((w*(((1-Y)-p)**2)).sum()/w.sum()))\n",
    "#np.mean(-Y * np.log(p) - (1-Y) *np.log(1-p+1e-12)) + 0.1*((w*(((1-Y)-p)**2)).sum()/w.sum()) + 1e-3 * (W[:-1]**2).sum()\n",
    "#return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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